LIU, Rulin; HAO, Junjie; LI, Jiagen; WANG, Shujie; LIU, Haochen; ZHOU, Ziming; DELVILLE, Marie-Hélène; CHENG, Jiaji; WANG, Kai; ZHU, Xi

doi:10.1021/acs.jpclett.0c02115

Métadonnées

Licence d’utilisation du document

LIU, Rulin
Shenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS]

HAO, Junjie
Department of Electrical and Electronic Engineering
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]

LI, Jiagen
Shenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS]

Langue

Article de revue

Ce document a été publié dans

Journal of Physical Chemistry Letters. 2020, vol. 11, p. 7232-7238

American Chemical Society

Résumé en anglais

The synthesis of CdSe/CdS core/shell nanoparticles was revisited with the help of a causal inference machine learning framework. The tadpole morphology with 1–2 tails was experimentally discovered. The causal inference model revealed the causality between the oleic acid (OA), octadecylphosphonic acid (ODPA) ligands, and the detailed tail shape of the tadpole morphology. Further, with the identified causality, a neural network was provided to predict and directly lead to the original experimental discovery of new tadpole-shaped structures. An entropy-driven nucleation theory was developed to understand both the ligand and temperature dependent experimental data and the causal inference from the machine learning framework. This work provided a vivid example of how the artificial intelligence technology, including machine learning, could benefit the materials science research for the discovery.< Réduire

Mots clés en anglais

Morphology

Ligands

Theoretical and computational chemistry

Transmission electron microscopy

Neural networks

Métadonnées

Licence d’utilisation du document

Causal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles

Langue

Ce document a été publié dans

Résumé en anglais

Mots clés en anglais

DOI

Origine

Unités de recherche